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Behavior recognition method based on robust relative attributes

A technology of attributes and behaviors, applied in the field of intelligent video surveillance, to achieve the effect of improving robustness

Active Publication Date: 2013-10-09
INST OF AUTOMATION CHINESE ACAD OF SCI
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But in real life, binary attributes cannot accurately describe behaviors

Method used

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  • Behavior recognition method based on robust relative attributes
  • Behavior recognition method based on robust relative attributes
  • Behavior recognition method based on robust relative attributes

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Embodiment Construction

[0013] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0014] figure 1 is the flowchart of the behavior recognition method based on the robust relative attribute of the present invention, such as figure 1 Said, said method comprises the following steps:

[0015] Step S1, extracting the feature vector of each action video sample in the video sample library;

[0016] Said step S1 further comprises the following steps:

[0017] Step S11, utilizing three-dimensional corner feature (Harris3D) to extract a plurality of spatiotemporal interest points for each action video sample in the video sample library;

[0018] Step S12, extracting a gradient histogram (histogramoforientedgradients, HOG) and an optical flow histogram (histogramofopticalflow, HOF) around each extracted spatio...

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Abstract

The invention discloses a behavior recognition method based on robust relative attributes. The method includes the following steps of abstracting feature vectors of all movement video samples in a video sample bank, setting a plurality of human body movement attributes corresponding to various human body behaviors, setting a relationship of every two movement video representing human body behaviors, namely, the relationship of each movement video pair, under all the human body movement attributes, treating the relationship of each movement video pair as input, conduct training through a sequencing support vector machine to obtain a training model, solving the sequencing support vector machine through a gradient descent method to obtain parameter vectors of the sequencing support vector machine and further obtain the optimal training model, and conducting human body behavior recognition on each piece of movement video to be tested through the obtained optimal training model to obtain a human body movement recognition result. Experimental results show that the behavior recognition method can improve robustness of the human body behavior recognition.

Description

technical field [0001] The invention belongs to the technical field of intelligent video monitoring, and in particular relates to a behavior recognition method based on robust relative attributes. Background technique [0002] Behavior recognition plays an important role in video surveillance. It can recognize the behavior of human body in the video, and contribute to dangerous behavior alarm and specific behavior recognition. The simplest and most effective method in behavior recognition is the method based on the bag-of-words (BOW) model. This method first extracts the features of the video, then clusters all the features, and then according to each video feature The frequency of occurrence at the cluster centers is histogrammed. But a shortcoming of this method is that it does not take spatio-temporal characteristics into account. Zhang et al. used a semantic-based linear coding method to not only consider the spatio-temporal relationship between features but also reduc...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
Inventor 王春恒张重肖柏华周文刘爽
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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